Automated Data Capture in Indoor Construction Sites

Automated Data Capture in Indoor Construction Sites

Reliable objective (i.e., cost, time, and scope) assessment of architecture, engineering, and construction (AEC) projects requires reliable data with respect to the as-built conditions. Manual data acquisition in such continually changing environments is laborious, costly, and error-prone. This project aims to develop an automated data capture system for indoor construction environments using multi-sensor autonomous unmanned aerial vehicles (UAVs). This requires the use of autonomous navigation, localization, and mapping algorithms in a GPS-denied indoor environment, while incorporating and updating the information available in the building information model (BIM). The application of such a system includes automated visual progress monitoring, visual inspection, quality control, and BIM updating.

 

Related Publications

Improved tag-based indoor localization of UAVs using extended Kalman filter
N. Kayhani, A. Heins, W. Zhao, M. Nahangi, B. McCabe, and A. P. Schoellig
in Proc. of the International Symposium on Automation and Robotics in Construction (ISARC), 2019. Accepted.
[View BibTeX] [View Abstract] [Download PDF]

Indoor localization and navigation of unmanned aerial vehicles (UAVs) is a critical function for autonomous flight and automated visual inspection of construction elements in continuously changing construction environments. The key challenge for indoor localization and navigation is that the global positioning system (GPS) signal is not sufficiently reliable for state estimation. Having used the AprilTag markers for indoor localization, we showed a proof-of-concept that a camera-equipped UAV can be localized in a GPS-denied environment; however, the accuracy of the localization was inadequate in some situations. This study presents the implementation and performance assessment of an Extended Kalman Filter (EKF) for improving the estimation process of a previously developed indoor localization framework using AprilTag markers. An experimental set up is used to assess the performance of the updated estimation process in comparison to the previous state estimation method and the ground truth data. Results show that the state estimation and indoor localization are improved substantially using the EKF. To have a more robust estimation, we extract and fuse data from multiple tags. The framework can now be tested in real-world environments given that our continuous localization is sufficiently robust and reliable.

@INPROCEEDINGS{kayhani-isarc19,
author = {Navid Kayhani and Adam Heins and Wenda Zhao and Mohammad Nahangi and Brenda McCabe and Angela P. Schoellig},
title = {Improved Tag-based Indoor Localization of {UAV}s Using Extended {Kalman} Filter},
booktitle = {{Proc. of the International Symposium on Automation and Robotics in Construction (ISARC)}},
year = {2019},
note = {Accepted},
abstract = {Indoor localization and navigation of unmanned aerial vehicles (UAVs) is a critical function for autonomous flight and automated visual inspection of construction elements in continuously changing construction environments. The key challenge for indoor localization and navigation is that the global positioning system (GPS) signal is not sufficiently reliable for state estimation. Having used the AprilTag markers for indoor localization, we showed a proof-of-concept that a camera-equipped UAV can be localized in a GPS-denied environment; however, the accuracy of the localization was inadequate in some situations. This study presents the implementation and performance assessment of an Extended Kalman Filter (EKF) for improving the estimation process of a previously developed indoor localization framework using AprilTag markers. An experimental set up is used to assess the performance of the updated estimation process in comparison to the previous state estimation method and the ground truth data. Results show that the state estimation and indoor localization are improved substantially using the EKF. To have a more robust estimation, we extract and fuse data from multiple tags. The framework can now be tested in real-world environments given that our continuous localization is sufficiently robust and reliable.},
}

Automated localization of UAVs in GPS-denied indoor construction environments using fiducial markers
M. Nahangi, A. Heins, B. McCabe, and A. P. Schoellig
in Proc. International Symposium on Automation and Robotics in Construction (ISARC), 2018, pp. 88-94.
[View BibTeX] [View Abstract] [Download PDF]

Unmanned Aerial Vehicles (UAVs) have opened a wide range of opportunities and applications in different sectors including construction. Such applications include: 3D mapping from 2D images and video footage, automated site inspection, and performance monitoring. All of the above-mentioned applications perform well outdoors where GPS is quite reliable for localization and navigation of UAV’s. Indoor localization and consequently indoor navigation have remained relatively untapped, because GPS is not sufficiently reliable and accurate in indoor environments. This paper presents a method for localization of aerial vehicles in GPS-denied indoor construction environments. The proposed method employs AprilTags that are linked to previously known coordinates in the 3D building information model (BIM). Using cameras on-board the UAV and extracting the transformation from the tag to the camera’s frame, the UAV can be localized on the site. It can then use the previously computed information for navigation between critical locations on construction sites. We use an experimental setup to verify and validate the proposed method by comparing with an indoor localization system as the ground truth. Results show that the proposed method is sufficiently accurate to perform indoor navigation. Moreover, the method does not intensify the complexity of the construction execution as the tags are simply printed and placed on available surfaces at the construction site.

@INPROCEEDINGS{nahangi-isarc18,
author={Mohammad Nahangi and Adam Heins and Brenda McCabe and Angela P. Schoellig},
title={Automated Localization of {UAV}s in {GPS}-Denied Indoor Construction Environments Using Fiducial Markers},
booktitle = {{Proc. International Symposium on Automation and Robotics in Construction (ISARC)}},
year = {2018},
pages={88--94},
abstract = {Unmanned Aerial Vehicles (UAVs) have opened a wide range of opportunities and applications in different sectors including construction. Such applications include: 3D mapping from 2D images and video footage, automated site inspection, and performance monitoring. All of the above-mentioned applications perform well outdoors where GPS is quite reliable for localization and navigation of UAV’s. Indoor localization and consequently indoor navigation have remained relatively untapped, because GPS is not sufficiently reliable and accurate in indoor environments. This paper presents a method for localization of aerial vehicles in GPS-denied indoor construction environments. The proposed method employs AprilTags that are linked to previously known coordinates in the 3D building information model (BIM). Using cameras on-board the UAV and extracting the transformation from the tag to the camera’s frame, the UAV can be localized on the site. It can then use the previously computed information for navigation between critical locations on construction sites. We use an experimental setup to verify and validate the proposed method by comparing with an indoor localization system as the ground truth. Results show that the proposed method is sufficiently accurate to perform indoor navigation. Moreover, the method does not intensify the complexity of the construction execution as the tags are simply printed and placed on available surfaces at the construction site.},
}

University of Toronto Institute for Aerospace Studies